Facial recognition for data visualization

Yesterday, I discovered a very interesting capability in R. There’s a package called aplpack that allows you to plot Chernoff faces. Chernoff faces is a way of presenting multivariate data in which the output looks like the faces of cartoon characters. One thing that I immediately noticed was that in using this package, I could quickly recognize (at a glance) aspects of the dataset that are similar or dissimilar.

Following the example of the article I read, I tested it using the built-in dataset in R known as mtcars (Motor Trend Car Road Tests), which looks like this:

                                  mpg cyl  disp  hp drat    wt  qsec vs am gear carb

Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4

Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4

Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1

Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1

Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2

Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1

Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4

Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2

Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2

Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4

Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4

Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3

Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3

Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3

Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4

Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4

Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4

Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1

Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2

Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1

Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1

Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2

AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2

Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4

Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2

Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1

Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2

Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2

Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4

Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6

Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

When you run the appropriate function in aplpack, the dataset now looks like this:

faces

With the visualization of the data , one can quickly see which cars in the dataset are similar in the variables tested.

Neat eh?

For more information on this, take a look at the original blog post.

Advertisements

1 Comment

Filed under Computers & Internet

One response to “Facial recognition for data visualization

Your comments:

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s